Handwriting recognition is an image classification problem, and this paper proposes the transfer learning and convolution neural network for this task [1]}, [2]}. In particular, instead of designing a new deep convolutional neural network with the random initialization, we exploited existing trained DCNN on 1000 objects as a starting point to identify Devanagari alphabets. We implemented AlexNet [3]}; DenseNet 121 and DenseNet 201 [4]}; Vgg 11, Vgg 16, and Vgg 19 [5]}; and Inception V3 [6]}. Training an entire DCNN with random initialization is highly computationally intensive and may not be practically feasible due to a limited dataset. Transfer Learning allows using the pretrained ConvNet either as initialization or fixed feature extractor. The pre-trained models are generally trained on ImageNet that has around 1.2 million images with 1000 different categories.
